Consumer profiling and advertisement selection system

ABSTRACT

A consumer profiling and advertisement selection system is presented in which consumers or subscribers can be characterized based on their purchase or viewing habits. The result of this process is a consumer characterization vector describing the probabilistic demographics and product preferences of the subscriber or viewer. Advertisement characterization vectors describing an actual or hypothetical market for a product or desired viewing audience can be determined. The ad characteristics including an ad demographic vector, an ad product category and an ad product preference vector is transmitted along with a consumer ID. The consumer ID is used to retrieve a consumer characterization vector which is correlated with the ad characterization vector to determine the suitability of the advertisement to the consumer. A price for displaying the advertisement can be determined based on the results of the correlation of the ad characteristics with the consumer characterization vector.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.09/807,887, filed Apr. 19, 2001, and entitled Consumer Profiling andAdvertisement Selection System, the entire disclosure of which isincorporated herein by reference.

This application claims the benefit of International Application No.PCT/US99/28628, filed Dec. 2, 1999, entitled Consumer Profiling andAdvertisement Selection System, which claims the benefit of co-pendingU.S. patent application Ser. No. 09/204,888, filed Dec. 3, 1998,entitled Subscriber Characterization System; U.S. patent applicationSer. No. 09/268,526, filed Mar. 12, 1999, entitled AdvertisementSelection System Supporting Discretionary Target Market Characteristics,now U.S. Pat. No. 6,216,129; and U.S. patent application Ser. No.09/268,519, filed Mar. 12, 1999, entitled Consumer Profiling System, nowU.S. Pat. No. 6,298,348.

BACKGROUND OF THE INVENTION

The advent of the Internet has resulted in the ability to communicatedata across the globe instantaneously, and will allow for numerous newapplications which enhance consumer's lives. One of the enhancementswhich can occur is the ability for the consumer to receive advertisingwhich is relevant to their lifestyle, rather than a stream of adsdetermined by the program they are watching. Such “targeted ads” canpotentially reduce the amount of unwanted information which consumersreceive in the mail, during television programs, and when using theInternet. Examples of editorial targeting can be found on the World WideWeb, where banners are delivered based on the page content. The productliterature from DoubleClick, “Dynamic Advertising Reporting andTargeting (DART),” printed from the World Wide Web sitehttp://www.doubleclick.net/dart on Jun. 19, 1998 discloses DoubleClick'sadvertising solution for matching advertiser's selected targetedprofiles with individual user profiles and deliver an appropriatebanner. The user and advertisements are matched based on geographiclocation or keywords on the page content. The product literature fromImgis, “Ad Force,” printed from the World Wide Web sitehttp://www.starpt.com/core on Jun. 30, 1998 discloses an ad managementsystem for targeting users and delivering advertisements to them. Usersare targeted based on the type of content they are viewing or bykeywords.

From an advertiser's perspective the ability to target ads can bebeneficial since they have some confidence that their ad will at leastbe determined relevant by the consumer, and therefore will not be foundannoying because it is not applicable to their lifestyle. Differentsystems for matching a consumer profile to an advertisement have beenproposed such as the U.S. Pat. No. 5,774,170, which discloses a systemfor delivering targeted advertisement to consumers. In this system, aset of advertisements is tagged with commercial identifier (CID) and,from the existing marketing database, a list of prospective viewers isalso identified with CID. The commercials are displayed to the consumerswhen the CIDs match.

Other systems propose methods for delivering programming tailored tosubscribers' profile. U.S. Pat. No. 5,446,919 discloses a communicationsystem capable of targeting a demographically or psychographicallydefined audience. Demographic and psychographic information aboutaudience member are downloaded and stored in the audience memberreceiver. Media messages are transmitted to audience member along with aselection profile command, which details the demographic/psychographicprofile of audience members that are to receive each media message.Audience members which fall within a group identified by the selectionprofile command are presented with the media message.

U.S. Pat. No. 5,223,924 discloses a system and method for automaticallycorrelating user preferences with a TV program information database. Thesystem includes a processor that performs “free text” search techniquesto correlate the downloaded TV program information with the viewer'spreferences. U.S. Pat. No. 5,410,344 discloses a method for selectingaudiovideo programs based on viewers' preferences, wherein each of theaudiovideo programs has a plurality of programs attributes and acorresponding content code representing the program attributes. Themethod comprises the steps of storing a viewer preference file, whichincludes attributes ratings, which represents the degree of impact ofthe programs attributes on the viewer and, in response to the comparisonof viewer preference file with the program content codes, a program isselected for presentation to the viewer.

In order to determine the applicability of an advertisement to aconsumer, it is necessary to know something about their lifestyle, andin particular to understand their demographics (age, household size andincome). In some instances, it is useful to know their particularpurchasing habits. Purchasing habits are being used by E-commerce toprofile their visitors. As an example, the product literature from Aptexsoftware Inc., “SelectCast for Commerce Servers,” printed from the WorldWide Web site http://www.aptex.com/products-selectcast-commerce.htm onJun. 30, 1998 discloses the product SelectCast for Commerce Servers. Theproduct personalizes online shopping based on observed user behavior.User interests are learned based on the content they browse, thepromotions they click and the products they purchase.

Knowledge of the purchasing habits of a consumer can be beneficial to aproduct vendor in the sense that a vendor of soups would like to knowwhich consumers are buying their competitor's soup, so that they cantarget ads at those consumers in an effort to convince them to switchbrands. That vendor will probably not want to target loyal customers,although for a new product introduction the strategy may be to convinceloyal customers to try the new product. In both cases it is extremelyuseful for the vendor to be able to determine what brand of product theconsumer presently purchases.

There are several difficulties associated with the collection,processing, and storage of consumer data. First, collecting consumerdata and determining the demographic parameters of the consumer can bedifficult. Surveys can be performed, and in some instances the consumerwill willingly give access to normally private data including familysize, age of family members, and household income. In such circumstancesthere generally needs to be an agreement with the consumer regarding howthe data will be used. If the consumer does not provide this datadirectly, the information must be “mined” from various pieces ofinformation which are gathered about the consumer, typically fromspecific purchases.

A relatively intrusive method for collecting consumer information isdescribed in U.S. Pat. No. 4,546,382, which discloses a television andmarket research data collection system and method. A data collectionunit containing a memory, stores data as to which of the plurality of TVmodes are in use, which TV channel is being viewed as well as input froma suitable optical scanning device for collecting consumer productpurchases.

Once data is collected, usually from one source, some type of processingcan be performed to determine a particular aspect of the consumer'slife. As an example, processing can be performed on credit data todetermine which consumers are a good credit risk and have recentlyapplied for credit. The resulting list of consumers can be solicited,typically by direct mail. Although information such as credit history isstored on multiple databases, storage of other information such as thespecifics of grocery purchases is not typically performed. Even if eachindividual's detailed list of grocery purchases was recorded, theinformation would be of little use since it would amount to nothing morethan unprocessed shopping lists.

Privacy concerns are also an important factor in using consumer purchaseinformation. Consumers will generally find it desirable thatadvertisements and other information is matched with their interests,but will not allow indiscriminate access to their demographic profileand purchase records.

The Internet has spawned the concept of “negatively priced information”in which consumers can be paid to receive advertising. Paying consumersto watch advertisements can be accomplished interactively over theInternet, with the consumer acknowledging that they will watch anadvertisement for a particular price. Previously proposed schemes suchas that described in U.S. Pat. No. 5,794,210, entitled “AttentionBrokerage,” of which A. Nathaniel Goldhaber and Gary Fitts are theinventors, describe such a system, in which the consumer is presentedwith a list of advertisements and their corresponding payments. Theconsumer chooses from the list and is compensated for viewing theadvertisement. The system uses also software agents representingconsumers to match the consumer interest profiles with advertisements.The matching is done using “relevance indexing” which is based onhierarchical tree structures. The system requires real-timeinteractivity in that the viewer must select the advertisement from thelist of choices presented.

The ability to place ads to consumers and compensate them for viewingthe advertisements opens many possibilities for new models ofadvertising. However, it is important to understand the demographics andproduct preferences of the consumer in order to be able to determine ifan advertisement is appropriate.

Although it is possible to collect statistical information regardingconsumers of particular products and compare those profiles againstindividual demographic data points of consumers, such a methodology onlyallows for selection of potential consumers based on the demographics ofexisting customers of the same or similar products.

U.S. Pat. No. 5,515,098, entitled “System and method for selectivelydistributing commercial messages over a communications network,” ofwhich John B. Carles is the inventor, describes a method in which targethousehold data of actual customers of a product are compared againstsubscriber household data to determine the applicability of a commercialto a household. Target households for a product or service arecharacterized by comparing or correlating the profile of the customerhousehold to the profile of all households. A rating is established foreach household for each category of goods/services. The householdswithin a predefined percentile of subscribers, as defined by the rating,are targeted by the advertiser of the product or service.

It will also frequently be desirable to target an advertisement to amarket having discretionary characteristics and to obtain a measure ofthe correlation of these discretionary features with probabilistic ordeterministic data of the consumer/subscriber, rather than being forcedto rely on the characteristics of existing consumers of a product. Suchcorrelation should be possible based both on demographic characteristicsand product preferences.

Another previously proposed system, described in U.S. Pat. No.5,724,521, entitled “Method and apparatus for providing electronicadvertisements to end users in a consumer best-fit pricing manner,” ofwhich R. Dedrick is the inventor, utilizes a consumer scale as themechanism to determine to which group an advertisement is intended. Aconsumer scale matching process compares the set of characteristicsstored in a user profile database to a consumer scale associated withthe electronic advertisement. The fee charged to the advertiser isdetermined by where the set of characteristics fall on the consumerscale. Such a system requires specification of numerous parameters andweighting factors, and requires access to specific and non-statisticalpersonal profile information.

For the foregoing reasons, there is a need for a consumer profilingsystem which can profile the consumer, provide access to the consumerprofile in a secure manner, and return a measurement of the potentialapplicability of an advertisement. There is also a need for anadvertisement selection system which can match an advertisement withdiscretionary target market characteristics, and which can do so in amanner which protects the privacy of the consumer data andcharacterizations.

SUMMARY OF THE INVENTION

The present invention supports the receipt of consumer purchaseinformation with which consumer characterization vectors are updatedbased on product characterization information. The consumercharacterization vectors include a consumer demographic vector whichprovides a probabilistic measure of the demographics of the consumer,and a product preference vector which describes which products theconsumer has typically purchased in the past, and therefore is likely topurchase in the future. The product characterization informationincludes vector information which represents probabilisticdeterminations of the demographics of purchasers of an item, heuristicrules which can be applied to probabilistically describe thedemographics of the consumer based on that purchase, and a vectorrepresentation of the purchase itself.

In a preferred embodiment a computer-readable detailed purchase recordis received, along with a unique consumer identifier. A demographiccharacterization vector corresponding to the consumer can be retrieved.In the event that there is no existing demographic characterizationvector for that consumer, a new demographic characterization vector canbe created. In a preferred embodiment the new demographiccharacterization vector contains no information. A set of heuristicrules is retrieved and contains a probabilistic measure of thedemographic characteristics of a typical purchaser of an item. A newdemographic characterization vector is calculated based on the purchase,the existing demographic characterization vector, and the heuristicrules.

In a preferred embodiment the calculation of the demographiccharacterization vector is performed by calculating a weighted averageof a product demographics vector and the existing demographiccharacterization vector. A weighting factor is used in which theweighting factor is determined based on the ratio of the current productpurchase amount to a cumulative product purchase amount. The cumulativeproduct purchase amount can be measured as the amount spent on aparticular category of items (e.g. groceries, clothes, accessories) overa given period of time such as one month or one year.

In a preferred embodiment the heuristic rules are in the form of aproduct demographics vector which states the demographics of knownpurchasers of an item. Each product can have an associated productdemographics vector.

The present invention can be used to develop product preferencedescriptions of consumers which describe the brand and size product thatthey purchase, and which provide a probabilistic interpretation of theproducts they are likely to buy in the future. The product preferencedescription can be generated by creating a weighted average of anexisting product preference vector describing the consumer's historicalproduct preferences (type of product, brand, and size) and thecharacteristics of recent purchases.

The present invention can be realized as a data processing system orcomputer program which processes consumer purchase records and updatestheir demographic and product preference profiles based on the use ofproduct characterization information. The data processing system canalso be used to receive information regarding an advertisement and toperform a correlation between the advertisement and the consumer'sdemographic and product preferences.

The present invention can be realized as software resident on one ormore computers. The system can be realized on an individual computerwhich receives information regarding consumer purchases, or can berealized on a network of computers in which portions of the system areresident on different computers.

One advantage of the present invention is that it allows consumerprofiles to be updated automatically based on their purchases, and formsa description of the consumer including demographic characteristics andproduct preferences. This description can be used by advertisers todetermine the suitability of advertisements to the consumer. Consumersbenefit from the system since they will receive advertisements which aremore likely to be applicable to them.

The present invention can be used to profile consumers to support thecorrelation of an advertisement characterization vector associated withan advertisement with the consumer characterization vector to determinethe applicability of the advertisement to the consumer.

Another feature of the present invention is the ability to price accessto the consumer based on the degree of correlation of an advertisementwith their profile. If an advertisement is found to be very highlycorrelated with a consumer's demographics and product preferences, arelatively high price can be charged for transmitting the advertisementto the consumer. From the consumer's perspective, if the correlationbetween the advertisement and the consumer's demographics or productpreferences is high the consumer can charge less to view the ad, sinceit is likely that is will be of interest.

The present invention also describes a system for determining theapplicability of an advertisement to a consumer, based on the receptionof an ad characterization vector and use of a unique consumer ID. Theconsumer ID is used to retrieve a consumer characterization vector, andthe correlation between the consumer characterization vector and the adcharacterization vector is used to determine the applicability of theadvertisement to the consumer. The price to be paid for presentation ofthe advertisement can be determined based on the degree of correlation.

The price to present an advertisement can increase with correlation, asmay be typical when the content/opportunity provider is also theprofiling entity. The price can decrease with correlation when theconsumer is the profiler, and is interested in, and willing to chargeless for seeing advertisements which are highly correlated with theirdemographics, lifestyle, and product preferences.

The present invention can be used to specify purchasers of a specificproduct. In a preferred embodiment the advertisement characterizationvector contains a description of a target market including an indicatorof a target product, i.e., purchasers of a particular product type,brand, or product size. The advertisement characterization vector iscorrelated with a consumer characterization vector which is retrievedbased on a unique consumer ID. The correlation factor is determined andindicates if the consumer is a purchaser of the product theadvertisement is intended for. This feature can be used to identifypurchasers of a particular brand and can be used to target ads at thoseconsumers to lure them away from their present product provider.Similarly, this feature can be used to target ads to loyal consumers tointroduce them to a new product in a product family, or different sizeof product.

One advantage of the present invention is that discretionary targetmarket parameters can be specified and do not necessarily need tocorrespond to an existing market, but can reflect the various marketsegments for which the advertisement is targeted. The market segmentscan be designated by demographic characteristics or by productpreferences.

Another advantage of the present invention is that demographic samplesof present purchasers of a product are not required to define the targetmarket.

The present invention can be used to determine the applicability of anadvertisement to a consumer based on demographics, product preferences,or a combination of both.

In a preferred embodiment of the present invention the correlation iscalculated as the scalar product of the ad characterization vector andthe consumer characterization vector. The ad characterization vector andconsumer characterization vector can be composed of demographiccharacteristics, product purchase characteristics, or a combination ofboth.

In a preferred embodiment pricing for the displaying of saidadvertisement is developed based on the result of the correlationbetween the ad characterization vector and the consumer characterizationvector. In a first embodiment the pricing increases as a function of thecorrelation. This embodiment can represent the situation in which theparty which determines the correlation also controls the ability todisplay the advertisement.

In an alternate embodiment the price for displaying the advertisementdecreases as a function of the degree of correlation. This embodimentcan represent the situation in which the consumer controls access to theconsumer characterization vector, and charges less to viewadvertisements which are highly correlated with their interests anddemographics. A feature of this embodiment is the ability of theconsumer to decrease the number of unwanted advertisements by charging ahigher price to view advertisements which are likely to be of lessinterest.

One advantage of the present invention is that it allows advertisementsto be directed to new markets by setting specific parameters in the adcharacterization vector, and does not require specific statisticalknowledge regarding existing customers of similar products.

Another advantage is that the system allows ads to be directed atconsumers of a competing brand, or specific targeting at loyalcustomers. This feature can be useful for the introduction of a newproduct to an existing customer base.

Another advantage of the present invention is that the correlation canbe performed by calculating a simple scalar (dot) product of the adcharacterization and consumer characterization vectors. A weighted sumor other statistical analysis is not required to determine theapplicability of the advertisement.

The present invention can be realized as a data processing system and asa computer program. The invention can be realized on an individualcomputer or can be realized using distributed computers with portions ofthe system operating on various computers.

An advantage of the present invention is the ability to directadvertisements to consumers which will find the advertisements ofinterest. This eliminates unwanted advertisements. Another advantage isthe ability of advertisers to target specific groups of potentialcustomers.

These and other features and objects of the invention will be more fullyunderstood from the following detailed description of the preferredembodiments which should be read in light of the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and form a part ofthe specification, illustrate the embodiments of the present inventionand, together with the description serve to explain the principles ofthe invention.

In the drawings:

FIGS. 1A and 1B show user relationship diagrams for the presentinvention;

FIGS. 2A, 2B, 2C and 2D illustrate a probabilistic consumer demographiccharacterization vector, a deterministic consumer demographiccharacterization vector, a consumer product preference characterizationvector, and a storage structure for consumer characterization vectorsrespectively;

FIGS. 3A and 3B illustrate an advertisement demographic characterizationvector and an advertisement product preference characterization vectorrespectively;

FIG. 4 illustrates a computer system on which the present invention canbe realized;

FIG. 5 illustrates a context diagram for the present invention;

FIGS. 6A and 6B illustrate pseudocode updating the characteristicsvectors and for a correlation operation respectively;

FIG. 7 illustrates heuristic rules;

FIGS. 8A and 8B illustrate flowcharts for updating consumercharacterization vectors and a correlation operation respectively; and

FIG. 9 represents pricing as a function of correlation.

FIG. 10 illustrates a representation of a consumer characterization as aset of basis vectors and an ad characterization vector.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In describing a preferred embodiment of the invention illustrated in thedrawings, specific terminology will be used for the sake of clarity.However, the invention is not intended to be limited to the specificterms so selected, and it is to be understood that each specific termincludes all technical equivalents which operate in a similar manner toaccomplish a similar purpose.

With reference to the drawings, in general, and FIGS. 1 through 10 inparticular, the method and apparatus of the present invention isdisclosed.

FIG. 1A shows a user relationship diagram which illustrates therelationships between a consumer profiling system and various entities.As can be seen in FIG. 1, a consumer 100 can receive information andadvertisements from a consumer personal computer (PC) 104, displayed ona television 108 which is connected to a settop 106, or can receive amailed ad 182.

Advertisements and information displayed on consumer PC 104 ortelevision 108 can be received over an Internet 150, or can be receivedover the combination of the Internet 150 with another telecommunicationsaccess system. The telecommunications access system can include but isnot limited to cable TV delivery systems, switched digital video accesssystems operating over telephone wires, microwave telecommunicationssystems, or any other medium which provides connectivity between theconsumer 100 and a content server 162 and ad server 146.

A content/opportunity provider 160 maintains the content server 162which can transmit content including broadcast programming across anetwork such as the Internet 150. Other methods of data transport can beused including private data networks and can connect the content sever160 through an access system to a device owned by consumer 100.

Content/opportunity provider 160 is termed such since if consumer 100 isreceiving a transmission from content server 162, thecontent/opportunity provider can insert an advertisement. For videoprogramming, content/opportunity provider is typically the cable networkoperator or the source of entertainment material, and the opportunity isthe ability to transmit an advertisement during a commercial break.

The majority of content that is being transmitted today is done so inbroadcast form, such as broadcast television programming (broadcast overthe air and via cable TV networks), broadcast radio, and newspapers.Although the interconnectivity provided by the Internet will allowconsumer specific programming to be transmitted, there will still be alarge amount of broadcast material which can be sponsored in part byadvertising. The ability to insert an advertisement in a broadcaststream (video, audio, or mailed) is an opportunity for advertiser 144.Content can also be broadcast over the Internet and combined withexisting video services, in which case opportunities for the insertionof advertisements will be present.

Although FIG. 1A represents content/opportunity provider 160 and contentserver 162 as being independently connected to Internet 150, with theconsumer's devices being also being directly connected to the Internet150, the content/opportunity provider 160 can also control access to thesubscriber. This can occur when the content/opportunity provider is alsothe cable operator or telephone company. In such instances, the cableoperator or telephone company can be providing content to consumer 100over the cable operator/telephone company access network. As an example,if the cable operator has control over the content being transmitted tothe consumer 100, and has programmed times for the insertion ofadvertisements, the cable operator is considered to be acontent/opportunity provider 160 since the cable operator can provideadvertisers the opportunity to access consumer 100 by inserting anadvertisement at the commercial break.

In a preferred embodiment of the present invention, a pricing policy canbe defined. The content/opportunity provider 160 can charge advertiser144 for access to consumer 100 during an opportunity. In a preferredembodiment the price charged for access to consumer 100 bycontent/opportunity provider varies as a function of the applicabilityof the advertisement to consumer 100. In an alternate embodimentconsumer 100 retains control of access to the profile and charges forviewing an advertisement.

The content provider can also be a mailing company or printer which ispreparing printed information for consumer 100. As an example, contentserver 162 can be connected to a printer 164 which creates a mailed ad182 for consumer 100. Alternatively, printer 164 can produceadvertisements for insertion into newspapers which are delivered toconsumer 100. Other printed material can be generated by printer 162 anddelivered to consumer 100 in a variety of ways.

Advertiser 144 maintains an ad server 146 which contains a variety ofadvertisements in the form of still video which can be printed, videoadvertisements, audio advertisements, or combinations thereof.

Profiler 140 maintains a consumer profile server 130 which contains thecharacterization of consumer 100. The consumer profiling system isoperated by profiler 140, who can use consumer profile server 130 oranother computing device connected to consumer profile server 130 toprofile consumer 100.

Data to perform the consumer profiling is received from a point ofpurchase 110. Point of purchase 110 can be a grocery store, departmentstore, other retail outlet, or can be a web site or other location wherea purchase request is received and processed. In a preferred embodiment,data from the point of purchase is transferred over a public or privatenetwork 120, such as a local area network within a store or a wide areanetwork which connects a number of department or grocery stores. In analternate embodiment the data from point of purchase 110 is transmittedover the Internet 150 to profiler 140.

Profiler 140 may be a retailer who collects data from its stores, butcan also be a third party who contracts with consumer 100 and theretailer to receive point of purchase data and profile consumer 100.Consumer 100 may agree to such an arrangement based on the increasedconvenience offered by targeted ads, or through a compensationarrangement in which they are paid on a periodic basis for revealingtheir specific purchase records.

Consumer profile server 130 can contain a consumer profile which isdetermined from observation of the consumer's viewing habits ontelevision 108 or consumer PC 104. Determination of demographic andproduct preference information based on the consumer's use of servicessuch as cable television and Internet access can be performed bymonitoring the channel selections that a subscriber makes, anddetermining household demographics based on the subscriber selectionsand information associated with the programming being watched.

In one embodiment the channel selections are recorded, and based on thetime of day during which the programming is watched and duration ofviewing, heuristic rules are applied to make probabilisticdeterminations regarding the household demographics including age,gender, household size and income, as illustrated in FIG. 2A. This canbe accomplished by applying heuristic rules which associate theprogramming with known and assumed characteristics for viewers of theprogramming. As an example, it is known that the probability that theviewer of a cartoon in the morning is in the 3-8 year old age group ishigh, thus if the household viewing habits consistently record viewingof cartoons the probability that the household will contain one or moreviewers in the 3-8 year old age group is high.

In one embodiment information regarding the program is extracted fromthe Electronic Program Guide (EPG) which contains information regardingthe scheduled programming. In another embodiment information regardingthe programming is retrieved from the closed caption channel transmittedin the broadcast signal.

The volume at which the program is watched can also be stored and formsan additional basis for subscriber characterization, wherein the mutingof a channel indicates limited interest in a particular program oradvertisement. In the case of an advertisement, muting of theadvertisement can be used as a measure of the effectiveness (orineffectiveness) of the advertisement and can serve as part of the basisfor the subscriber characterization. The muting of a program, as well asthe duration for which the program is watched, can also be used in thedetermination of the subscriber characterization vector.

By processing the recorded viewing habits in conjunction withprogramming related information and heuristic rules similar to thoseillustrated in FIG. 7 but related to programming rather than purchases,it is possible to construct a subscriber characterization vector whichcontains a probabilistic demographic profile of the household.

When used herein, the term consumer characterization vector alsorepresents the subscriber characterization vector previously described.Both the consumer characterization vector and the subscribercharacterization vector contain demographic and product preferenceinformation which is related to consumer 100.

FIG. 1B illustrates an alternate embodiment of the present invention inwhich the consumer 100 is also profiler 140. Consumer 100 maintainsconsumer profile server 130 which is connected to a network, eitherdirectly or through consumer PC 104 or settop 106. Consumer profileserver 130 can contain the consumer profiling system, or the profilingcan be performed in conjunction with consumer PC 104 or settop 106. Asubscriber characterization system which monitors the viewing habits ofconsumer 100 can be used in conjunction with the consumer profilingsystem to create a more accurate consumer profile.

When the consumer 100 is also the profiler 140, as shown in FIG. 1B,access to the consumer demographic and product preferencecharacterization is controlled exclusively by consumer 100, who willgrant access to the profile in return for receiving an increasedaccuracy of ads, for cash compensation, or in return for discounts orcoupons on goods and services.

FIG. 2A illustrates an example of a probabilistic demographiccharacterization vector. The demographic characterization vector is arepresentation of the probability that a consumer falls within a certaindemographic category such as an age group, gender, household size, orincome range.

In a preferred embodiment the demographic characterization vectorincludes interest categories. The interest categories may be organizedaccording to broad areas such as music, travel, and restaurants.Examples of music interest categories include country music, rock,classical, and folk. Examples of travel categories include “travels toanother state more than twice a year,” and travels by plane more thantwice a year.”

FIG. 2B illustrates a deterministic demographic characterization vector.The deterministic demographic characterization vector is arepresentation of the consumer profile as determined from deterministicrather than probabilistic data. As an example, if consumer 100 agrees toanswer specific questions regarding age, gender, household size, income,and interests the data contained in the consumer characterization vectorwill be deterministic.

As with probabilistic demographic characterization vectors, thedeterministic demographic characterization vector can include interestcategories. In a preferred embodiment, consumer 100 answers specificquestions in a survey generated by profiler 140 and administered overthe phone, in written form, or via the Internet 150 and consumer PC 104.The survey questions correspond either directly to the elements in theprobabilistic demographic characterization vector, or can be processedto obtain the deterministic results for storage in the demographiccharacterization vector.

FIG. 2C illustrates a product preference vector. The product preferencerepresents the average of the consumer preferences over past purchases.As an example, a consumer who buys the breakfast cereal manufactured byPost under the trademark ALPHABITS about twice as often as purchasingthe breakfast cereal manufactured by Kellogg under the trademark CORNFLAKES, but who never purchases breakfast cereal manufactured by GeneralMills under the trademark WHEATIES, would have a product preferencecharacterization such as that illustrated in FIG. 2C. As shown in FIG.2C, the preferred size of the consumer purchase of a particular producttype can also be represented in the product preference vector.

FIG. 2D represents a data structure for storing the consumer profile,which can be comprised of a consumer ID field 237, a deterministicdemographic data field 239, a probabilistic demographic data field 241,and one or more product preference data fields 243. As shown in FIG. 2D,the product preference data field 243 can be comprised of multiplefields arranged by product categories 253.

Depending on the data structure used to store the information containedin the vector, any of the previously mentioned vectors may be in theform of a table, record, linked tables in a relational database, seriesof records, or a software object.

The consumer ID 512 can be any identification value uniquely associatedwith consumer 100. In a preferred embodiment consumer ID 512 is atelephone number, while in an alternate embodiment consumer ID 512 is acredit card number. Other unique identifiers include consumer name withmiddle initial or a unique alphanumeric sequence, the consumer address,social security number.

The vectors described and represented in FIGS. 2A-C form consumercharacterization vectors that can be of varying length and dimension,and portions of the characterization vector can be used individually.Vectors can also be concatenated or summed to produce longer vectorswhich provide a more detailed profile of consumer 100. A matrixrepresentation of the vectors can be used, in which specific elements,such a product categories 253, are indexed. Hierarchical structures canbe employed to organize the vectors and to allow hierarchical searchalgorithms to be used to locate specific portions of vectors.

FIGS. 3A and 3B represent an ad demographics vector and an ad productpreference vector respectively. The ad demographics vector, similar instructure to the demographic characterization vector, is used to targetthe ad by setting the demographic parameters in the ad demographicsvector to correspond to the targeted demographic group. As an example,if an advertisement is developed for a market which is the 18-24 and24-32 age brackets, no gender bias, with a typical household size of2-5, and income typically in the range of $20,000-$50,000, the addemographics vector would resemble the one shown in FIG. 3A. The addemographics vector represents a statistical estimate of who the ad isintended for, based on the advertisers belief that the ad will bebeneficial to the manufacturer when viewed by individuals in thosegroups. The benefit will typically be in the form of increased sales ofa product or increased brand recognition. As an example, an “image ad”which simply shows an artistic composition but which does not directlysell a product may be very effective for young people, but may beannoying to older individuals. The ad demographics vector can be used toestablish the criteria which will direct the ad to the demographic groupof 18-24 year olds.

FIG. 3B illustrates an ad product preference vector. The ad productpreference vector is used to select consumers which have a particularproduct preference. In the example illustrated in FIG. 3B, the adproduct preference vector is set so that the ad can be directed apurchasers of ALPHABITS and WHEATIES, but not at purchasers of CORNFLAKES. This particular setting would be useful when the advertiserrepresents Kellogg and is charged with increasing sales of CORN FLAKES.By targeting present purchasers of ALPHABITS and WHEATIES, theadvertiser can attempt to sway those purchasers over to the Kelloggbrand and in particular convince them to purchase CORN FLAKES. Giventhat there will be a payment required to present the advertisement, inthe form of a payment to the content/opportunity provider 160 or to theconsumer 100, the advertiser 144 desires to target the ad and therebyincrease its cost effectiveness.

In the event that advertiser 144 wants to reach only the purchasers ofKellogg's CORN FLAKES, that category would be set at a high value, andin the example shown would be set to 1. As shown in FIG. 3B, productsize can also be specified. If there is no preference to size categorythe values can all be set to be equal. In a preferred embodiment thevalues of each characteristic including brand and size are individuallynormalized.

Because advertisements can be targeted based on a set of demographic andproduct preference considerations which may not be representative of anyparticular group of present consumers of the product, the adcharacterization vector can be set to identify a number of demographicgroups which would normally be considered to be uncorrelated. Becausethe ad characterization vector can have target profiles which are notrepresentative of actual consumers of the product, the adcharacterization vector can be considered to have discretionaryelements. When used herein the term discretionary refers to a selectionof target market characteristics which need not be representative of anactual existing market or single purchasing segment.

In a preferred embodiment the consumer characterization vectors shown inFIGS. 2A-C and the ad characterization vectors represented in FIGS. 3Aand 3B have a standardized format, in which each demographiccharacteristic and product preference is identified by an indexedposition. In a preferred embodiment the vectors are singly indexed andthus represent coordinates in n-dimensional space, with each dimensionrepresenting a demographic or product preference characteristic. In thisembodiment a single value represents one probabilistic or deterministicvalue (e.g. the probability that the consumer is in the 18-24 year oldage group, or the weighting of an advertisement to the age group).

In an alternate embodiment a group of demographic or productcharacteristics forms an individual vector. As an example, agecategories can be considered a vector, with each component of the vectorrepresenting the probability that the consumer is in that age group. Inthis embodiment each vector can be considered to be a basis vector forthe description of the consumer or the target ad. The consumer or adcharacterization is comprised of a finite set of vectors in a the vectorspace that describes the consumer or advertisement.

FIG. 4 shows the block diagram of a computer system for a realization ofthe consumer profiling system. A system bus 422 transports data amongstthe CPU 203, the RAM 204, Read Only Memory—Basic Input Output System(ROM-BIOS) 406 and other components. The CPU 203 accesses a hard drive400 through a disk controller 402. The standard input/output devices areconnected to the system bus 422 through the I/O controller 201. Akeyboard is attached to the I/O controller 201 through a keyboard port416 and the monitor is connected through a monitor port 418. The serialport device uses a serial port 420 to communicate with the I/Ocontroller 201. Industry Standard Architecture (ISA) expansion slots 408and Peripheral Component Interconnect (PCI) expansion slots 410 allowadditional cards to be placed into the computer. In a preferredembodiment, a network card is available to interface a local area, widearea, or other network. The computer system shown in FIG. 4 can be partof consumer profile server 130, or can be a processor present in anotherelement of the network.

FIG. 5 shows a context diagram for the present invention. Contextdiagrams are useful in illustrating the relationship between a systemand external entities. Context diagrams can be especially useful indeveloping object oriented implementations of a system, although use ofa context diagram does not limit implementation of the present inventionto any particular programming language. The present invention can berealized in a variety of programming languages including but not limitedto C, C++, Smalltalk, Java, Perl, and can be developed as part of arelational database. Other languages and data structures can be utilizedto realize the present invention and are known to those skilled in theart.

Referring to FIG. 5, in a preferred embodiment consumer profiling system500 is resident on consumer profile server 130. Point of purchaserecords 510 are transmitted from point of purchase 110 and stored onconsumer profile server 130. Heuristic rules records 530, pricing policy570, and consumer profile 560 are similarly stored on consumer profileserver 130. In a preferred embodiment advertisement records 540 arestored on ad server 146 and connectivity between advertisement records540 and consumer profiling system 500 is via the Internet or othernetwork.

In an alternate embodiment the entities represented in FIG. 5 arelocated on servers which are interconnect via the Internet or othernetwork.

Consumer profiling system 500 receives purchase information from a pointof purchase, as represented by point of purchase records 510. Theinformation contained within the point of purchase records 510 includesa consumer ID 512, a product ID 514 of the purchased product, thequantity 516 purchased and the price 518 of the product. In a preferredembodiment, the date and time of purchase 520 are transmitted by pointof purchase records 510 to consumer profiling system 500.

The consumer profiling system 500 can access the consumer profile 560 toupdate the profiles contained in it. Consumer profiling system 500retrieves a consumer characterization vector 562 and a productpreference vector 564. Subsequent to retrieval one or more dataprocessing algorithms are applied to update the vectors. An algorithmfor updating is illustrated in the flowchart in FIG. 8A. The updatedvectors termed herein as new demographic characterization vector 566 andnew product preference 568 are returned to consumer profile 560 forstorage.

Consumer profiling system 500 can determine probabilistic consumerdemographic characteristics based on product purchases by applyingheuristic rules 519. Consumer profiling system 500 provides a product ID514 to heuristic rules records 530 and receives heuristic rulesassociated with that product. Examples of heuristic rules areillustrated in FIG. 7.

In a preferred embodiment of the present invention, consumer profilingsystem 500 can determine the applicability of an advertisement to theconsumer 100. For determination of the applicability of anadvertisement, a correlation request 546 is received by consumerprofiling system 500 from advertisements records 540, along withconsumer ID 512. Advertisements records 540 also provides advertisementcharacteristics including an ad demographic vector 548, an ad productcategory 552 and an ad product preference vector 554.

Application of a correlation process, as will be described in accordancewith FIG. 8B, results in a demographic correlation 556 and a productcorrelation 558 which can be returned to advertisement records 540. In apreferred embodiment, advertiser 144 uses product correlation 558 anddemographic correlation 556 to determine the applicability of theadvertisement and to determine if it is worth purchasing theopportunity. In a preferred embodiment, pricing policy 570 is utilizedto determine an ad price 572 which can be transmitted from consumerprofiling system 500 to advertisement records 540 for use by advertiser144.

Pricing policy 570 is accessed by consumer profiling system 500 toobtain ad price 572. Pricing policy 570 takes into consideration resultsof the correlation provided by the consumer profiling system 500. Anexample of pricing schemes are illustrated in FIG. 9

FIGS. 6A and 6B illustrate pseudocode for the updating process and for acorrelation operation respectively. The updating process involvesutilizing purchase information in conjunction with heuristic rules toobtain a more accurate representation of consumer 100, stored in theform of a new demographic characterization vector 562 and a new productpreference vector 568.

As illustrated in the pseudocode in FIG. 6A the point of purchase datais read and the products purchased are integrated into the updatingprocess. Consumer profiling system 500 retrieves a product demographicsvector obtained from the set of heuristic rules 519 and applies theproduct demographics vector to the demographics characterization vector562 and the product preference vector 564 from the consumer profile 560.

The updating process as illustrated by the pseudocode in FIG. 6Autilizes a weighting factor which determines the importance of thatproduct purchase with respect to all of the products purchased in aparticular product category. In a preferred embodiment the weight iscomputed as the ratio of the total of products with a particular productID 514 purchased at that time, to the product total purchase, which isthe total quantity of the product identified by its product ID 514purchased by consumer 100 identified by its consumer ID 512, purchasedover an extended period of time. In a preferred embodiment the extendedperiod of time is one year.

In the preferred embodiment the product category total purchase isdetermined from a record containing the number of times that consumer100 has purchased a product identified by a particular product ID.

In an alternate embodiment other types of weighting factors, runningaverages and statistical filtering techniques can be used to use thepurchase data to update the demographic characterization vector. Thesystem can also be reset to clear previous demographic characterizationvectors and product preference vectors.

The new demographic characterization vector 566 is obtained as theweighted sum of the product demographics vector the demographiccharacterization vector 562. The same procedure is performed to obtainthe new product preference vector 568. Before storing those new vectors,a normalization is performed on the said new vectors. When used hereinthe term product characterization information refers productdemographics vectors, product purchase vectors or heuristic rules, allof which can be used in the updating process. The product purchasevector refers to the vector which represents the purchase of a itemrepresented by a product ID. As an example, a product purchase vectorfor the purchase of Kellogg's CORN FLAKES in a 32 oz. size has a productpurchase vector with a unity value for Kellogg's CORN FLAKES and in the32 oz. size. In the updating process the weighted sum of the purchase asrepresented by the product purchase vector is added to the productpreference vector to update the product preference vector, increasingthe estimated probability that the consumer will purchase Kellogg's CORNFLAKES in the 32 oz. size.

In FIG. 6B the pseudocode for a correlation process is illustrated.Consumer profiling system 500, after receiving the productcharacteristics and the consumer ID 512 from the advertisement recordsretrieves the consumer demographic characterization vector 562 and itsproduct preference vector 564. The demographic correlation is thecorrelation between the demographic characterization vector 562 and thead demographics vector. The product correlation is the correlationbetween the ad product preference vector 554 and the product preferencevector 564.

In a preferred embodiment the correlation process involves computing thedot product between vectors. The resulting scalar is the correlationbetween the two vectors.

In an alternate embodiment, as illustrated in FIG. 10, the basis vectorswhich describe aspects of the consumer can be used to calculate theprojections of the ad vector on those basis vectors. In this embodiment,the result of the ad correlation can itself be in vector form whosecomponents represent the degree of correlation of the advertisement witheach consumer demographic or product preference feature. As shown inFIG. 10 the basis vectors are the age of the consumer 1021, the incomeof the consumer 1001, and the family size of the consumer 1031. The adcharacterization vector 1500 represents the desired characteristics ofthe target audience, and can include product preference as well asdemographic characteristics.

In this embodiment the degree of orthogonality of the basis vectors willdetermine the uniqueness of the answer. The projections on the basisvectors form a set of data which represent the corresponding values forthe parameter measured in the basis vector. As an example, if householdincome is one basis vector, the projection of the ad characterizationvector on the household income basis vector will return a resultindicative of the target household income for that advertisement.

Because basis vectors cannot be readily created from some productpreference categories (e.g. cereal preferences) an alternaterepresentation to that illustrated in FIG. 2C can be utilized in whichthe product preference vector represents the statistical average ofpurchases of cereal in increasing size containers. This vector can beinterpreted as an average measure of the cereal purchased by theconsumer in a given time period.

The individual measurements of correlation as represented by thecorrelation vector can be utilized in determining the applicability ofthe advertisement to the subscriber, or a sum of correlations can begenerated to represent the overall applicability of the advertisement.

In a preferred embodiment individual measurements of the correlations,or projections of the ad characteristics vector on the consumer basisvectors, are not made available to protect consumer privacy, and onlythe absolute sum is reported. In geometric terms this can be interpretedas disclosure of the sum of the lengths of the projections rather thanthe actual projections themselves.

In an alternate embodiment the demographic and product preferenceparameters are grouped to form sets of paired scores in which elementsin the consumer characterization vector are paired with correspondingelements of the ad characteristics vector. A correlation coefficientsuch as the Pearson product-moment correlation can be calculated. Othermethods for correlation can be employed and are well known to thoseskilled in the art.

When the consumer characterization vector and the ad characterizationvector are not in a standardized format, a transformation can beperformed to standardize the order of the demographic and productpreferences, or the data can be decomposed into sets of basis vectorswhich indicate particular attributes such as age, income or family size.

FIG. 7 illustrates an example of heuristic rules including rules fordefining a product demographics vector. From the productcharacteristics, a probabilistic determination of household demographicscan be generated. Similarly, the monthly quantity purchased can be usedto estimate household size. The heuristic rules illustrated in FIG. 7serve as an example of the types of heuristic rules which can beemployed to better characterize consumer 100 as a result of theirpurchases. The heuristic rules can include any set of logic tests,statistical estimates, or market studies which provide the basis forbetter estimating the demographics of consumer 100 based on theirpurchases.

In FIG. 8A the flowchart for updating the consumer characterizationvectors is depicted. The system receives data from the point of purchaseat receive point of purchase information step 800. The system performs atest to determine if a deterministic demographic characterization vectoris available at deterministic demographic information available step 810and, if not, proceeds to update the demographic characteristics.

Referring to FIG. 8A, at read purchase ID info step 820, the product ID514 is read, and at update consumer demographic characterization vectorstep 830, an algorithm such as that represented in FIG. 6A is applied toobtain a new demographic characterization vector 566, which is stored inthe consumer profile 560 at store updated demographic characterizationvector step 840.

The end test step 850 can loop back to the read purchase ID info 820 ifall the purchased products are not yet processed for updating, orcontinue to the branch for updating the product preference vector 564.In this branch, the purchased product is identified at read purchase IDinfo step 820. An algorithm, such as that illustrated in FIG. 6A forupdating the product preference vector 564, is applied in update productpreference vector step 870. The updated vector is stored in consumerprofile 560 at store product preference vector step 880. This process iscarried out until all the purchased items are integrated in the updatingprocess.

FIG. 8B shows a flowchart for the correlation process. At step 900 theadvertisement characteristics described earlier in accordance with FIG.5 along with the consumer ID are received by consumer profiling system500. At step 910 the demographic correlation 556 is computed and at step920 the product preference correlation 558 is computed. An illustrativeexample of an algorithm for correlation is presented in FIG. 6 b. Thesystem returns demographic correlation 556 and product preferencecorrelation 558 to the advertisement records 540 before exiting theprocedure at end step 950.

FIG. 9 illustrates two pricing schemes, one for content/opportunityprovider 160 based pricing 970, which shows increasing cost as afunction of correlation. In this pricing scheme, the higher thecorrelation, the more the content/opportunity provider 160 charges toair the advertisement.

FIG. 9 also illustrates consumer based pricing 960, which allows aconsumer to charge less to receive advertisements which are more highlycorrelated with their demographics and interests.

As an example of the industrial applicability of the invention, aconsumer 100 can purchase items in a grocery store which also acts as aprofiler 140 using a consumer profiling system 500. The purchase recordis used by the profiler to update the probabilistic representation ofcustomer 100, both in terms of their demographics as well as theirproduct preferences. For each item purchased by consumer 100, productcharacterization information in the form of a product demographicsvector and a product purchase vector is used to update the demographiccharacterization vector and the product preference vector for consumer100.

A content/opportunity provider 160 may subsequently determine that thereis an opportunity to present an advertisement to consumer 100.Content/opportunity provider 160 can announce this opportunity toadvertiser 144 by transmitting the details regarding the opportunity andthe consumer ID 512. Advertiser 144 can then query profiler 140 bytransmitting consumer ID 512 along with advertisement specificinformation including the correlation request 546 and ad demographicsvector 548. The consumer profiling system 500 performs a correlation anddetermines the extent to which the ad target market is correlated withthe estimated demographics and product preferences of consumer 100.Based on this determination advertiser 144 can decide whether topurchase the opportunity or not.

Although this invention has been illustrated by reference to specificembodiments, it will be apparent to those skilled in the art thatvarious changes and modifications may be made which clearly fall withinthe scope of the invention. The invention is intended to be protectedbroadly within the spirit and scope of the appended claims.

1. A method of profiling users in an computer environment, said method comprising: (a) receiving a purchase history corresponding to a unique user and including information related to the purchase of at least one item by said unique user; (b) receiving demographic information corresponding to said unique user; (c) receiving product characterization information describing a statistical relationship between a particular product and demographic characteristics of purchasers of the product; (d) extrapolating additional demographic information from said purchase history and said product characterization information; and (e) updating said demographic information based on said additional demographic information.
 2. The method of claim 1, wherein said product characterization information is developed based on a market study of a user population that visits a particular website.
 3. The method of claim 1, wherein said demographic information is received from a user demographic profile corresponding to said unique user.
 4. The method of claim 3, further comprising: (f) identifying missing demographic information in said user demographic profile.
 5. The method of claim 5, wherein at least a portion of said missing demographic information in said user demographic profile is obtained through said additional demographic information.
 6. The method of claim 5, further comprising: (g) returning said updated demographic information of step (e) to said user demographic profile.
 7. The method of claim 1, wherein said product characterization information does not include information about specific users.
 8. The method of claim 1, further comprising: (f) targeting advertisements based on said updated demographic information.
 9. The method of claim 1, further comprising: (f) comparing said updated demographic information to a target expression; (g) generating a score based on said comparing; and (h) delivering an advertisement based on said score.
 10. A method of targeting ads in an computer environment based on user segmentation, said method comprising: (a) receiving user profile information corresponding to a unique user; (b) assigning, based on said user profile information, said unique user to a population segment; and (c) comparing said population segment of said unique user to an ad segment characterization corresponding to at least one advertisement.
 11. The method of claim 10, further comprising: (d) calculating a correlation factor between said ad segment characterization and said population segment of said unique user; and (e) targeting an ad based on said correlation factor.
 12. The method of claim 10, wherein said assigning is realized by creating a population segment vector that describes the population segment of said unique user.
 13. The method of claim 12, wherein said ad segment characterization is represented by an ad segment vector.
 14. The method of claim 13, wherein said comparing is realized by comparing said population segment vector to said ad segment vector.
 15. The method of claim 14, further comprising: (d) calculating a correlation factor between said ad segment vector and said population segment vector; and (e) targeting an ad based on said correlation factor.
 16. The method of claim 10, wherein said user profile information includes purchase history information.
 17. The method of claim 16, wherein said purchase history information includes a record of at least one purchase.
 18. The method of claim 10, wherein said user profile information includes demographic information.
 19. A method of presenting cross-sell products in a computer environment, said method comprising: (a) receiving a purchase history corresponding to a unique user and including information related to the purchase of at least one product; (b) receiving product characterization information for said at least one product wherein said product characterization describes a relationship between said at least one product and demographic characteristics of purchasers of said at least one product; (c) calculating a consumer characterization vector based on said purchase history and said product characterization information; and (d) suggesting items based on said consumer characterization vector.
 20. The method of claim 19, wherein said consumer characterization vector is based on a combination of a demographic characterization vector and a product preference vector.
 21. The method of claim 20, wherein the suggestion made in step (d) is based on the product preference vector.
 22. A method of profiling users in a computer environment, said method comprising: (a) receiving a purchase history wherein said purchase history corresponding to a unique user and said purchase history including information related to the purchase of at least one item by said unique user; (b) receiving demographic information corresponding to said unique user; (c) receiving product characterization information describing a statistical relationship between a particular product and demographic characteristics of purchasers of the product; (d) calculating additional demographic information from said purchase history and said product characterization information; and (e) updating said demographic information based on said additional demographic information. 